Generic Hierarchical Classification Using the Single-Link Clustering

Abstract

Up to date, research on automatic classification focused mainly on the efficiency of algorithms in regard to a given aspect of a dataset. The issue of classification of a given dataset in regard to various aspects is generally not addressed. In this chapter, a multi-facet hierarchical classification technique based on the single-link clustering is proposed. First, the single-link clustering is formally presented. Then, the concepts underlying the generic hierarchical classification technique are given. Next, analysis domains modeling a given facet of a dataset are described. A new language devoted to generate analysis domains is presented. Further, classification of analysis domains is discussed. Finally, examples of applications of the generic hierarchical classification are given.